Oil-price forecasting based on ARIMA, exponential smoothing, and autoregressive neural network models

Oil prices have an impact on the global economy. Demand shocks, such as those caused by the recent COVID-19 epidemic, or supply shocks caused by production reductions by major global producers, result in sudden price changes. Oil industry has been collecting price data since the late 19th century. A time series of this magnitude allows for consistent analysis of price behavior forecasts following demand or supply shocks. Financial time series are sensitive to exogenous shocks. From this perspective, this work presents a comparative analysis of predictive crude oil prices scenarios, obtained from a historical series of average annual prices. Two approaches were used: first, a combination of classic strategies based on exponential smoothing, and ARIMA models. Second, an autoregressive neural network model. Both approaches are complementary when used for long-term forecasting of oil prices and show good response to volatile data. Therefore, we are able to present an alternative data analysis, in a field where there is a great amount of relevant historical series, using probabilistic and non-linear models in order to observe predictions and make more effective decisions.